Neurological disorders are the leading cause of physical and cognitive disability across the globe, currently affecting approximately 15% of the worldwide population

Background: Cognitive impairment affects half of the multiple sclerosis (MS) patient population and is an important contributor to patients’ daily activities. Most cognitive impairment studies in MS are, however, cross-sectional or/and focused on the early disease stages. Objective: We aim to assess the time course of decline of different cognitive domains. Methods: We collected neuropsychological data on 514 MS patients to construct Kaplan-Meier survival curves of the tests included in the Neuropsychological Screening Battery for MS (NSBMS) and the Symbol Digit Modalities Test (SDMT). Cox-proportional hazard models were constructed to examine the influence of MS onset type, age at onset, gender, depression and level of education on the time course, expressed as age or disease. Results: Survival curves of tests focusing on information processing speed (IPS) declined significantly faster than tests with less specific demands of IPS. Median age for pathological decline was 56.2 years (95% CI: 54.4–58.2) on the SDMT and 63.9 years (95% CI: 60–66.9) on the CLTR, a memory task. Conclusion: In conclusion, IPS is the cognitive domain not only most widely affected by MS but it is also the first cognitive deficit to emerge in MS.

Background and purpose: Data from neuro‐imaging techniques allow us to estimate a brain's age. Brain age is easily interpretable as ‘how old the brain looks’ and could therefore be an attractive communication tool for brain health in clinical practice. This study aimed to investigate its clinical utility by investigating the relationship between brain age and cognitive performance in multiple sclerosis (MS).Methods: A linear regression model was trained to predict age from brain magnetic resonance imaging volumetric features and sex in a healthy control dataset (HC_train, n = 1673). This model was used to predict brain age in two test sets: HC_test (n = 50) and MS_test (n = 201). Brain‐predicted age difference (BPAD) was calculated as BPAD = brain age minus chronological age. Cognitive performance was assessed by the Symbol Digit Modalities Test (SDMT).Results: Brain age was significantly related to SDMT scores in the MS_test dataset (r = −0.46, p < 0.001) and contributed uniquely to variance in SDMT beyond chronological age, reflected by a significant correlation between BPAD and SDMT (r = −0.24, p < 0.001) and a significant weight (−0.25, p = 0.002) in a multivariate regression equation with age.Conclusions: Brain age is a candidate biomarker for cognitive dysfunction in MS and an easy to grasp metric for brain health.

Multi‐item working memory (WM) is a complex cognitive function thought to arise from specific frequency band oscillations and their interactions. While some theories and consistent findings have been established, there is still a lot of unclarity about the sources, temporal dynamics, and roles of event‐related fields (ERFs) and theta, alpha, and beta oscillations during WM activity. In this study, we performed an extensive whole‐brain ERF and time‐frequency analysis on n‐back magnetoencephalography data from 38 healthy controls. We identified the previously unknown sources of the n‐back M300, the right inferior temporal and parahippocampal gyrus and left inferior temporal gyrus, and frontal theta power increase, the orbitofrontal cortex. We shed new light on the role of the precuneus during n‐back activity, based on an early ERF and theta power increase, and suggest it to be a crucial link between lower‐level and higher‐level information processing. In addition, we provide strong evidence for the central role of the hippocampus in multi‐item WM behavior through the dynamics of theta and alpha oscillatory changes. Almost simultaneous alpha power decreases observed in the hippocampus and occipital fusiform gyri, regions known to be involved in letter processing, suggest that these regions together enable letter recognition, encoding and storage in WM. In summary, this study offers an extensive investigation into the spatial, temporal, and spectral characteristics of n‐back multi‐item WM activity.